39110394_JAGADEESH_P{Assignment_2}_Numpy_Exercise_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMcWEbFiLKvn"
      },
      "source": [
        "# NumPy Exercises \n",
        "\n",
        "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "39110394_JAGADEESH P\n"
      ],
      "metadata": {
        "id": "UmnpbDuDtPOe"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvBjXHM1LKvw"
      },
      "source": [
        "#### Import NumPy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "StInvTamLKv0"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qav3VCgyLKv5"
      },
      "source": [
        "#### Create an array of 10 zeros "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLlF23CdLKv6",
        "outputId": "3c3686cd-2fd5-450e-b643-985f7389eb0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "np.zeros(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-r7m8k4vLKv_"
      },
      "source": [
        "#### Create an array of 10 ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwuCBhvcLKwB",
        "outputId": "958f6268-4de3-4c8d-ee0a-bc545495b705"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "np.ones(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIcddv6lLKwO"
      },
      "source": [
        "#### Create an array of 10 fives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXDJ5b8cLKwU",
        "outputId": "5caca13d-55e6-4867-cc12-57b574b41b56"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "np.ones(10) * 5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UInvEwQLKwg"
      },
      "source": [
        "#### Create an array of the integers from 10 to 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9kAbHHiLKwj",
        "outputId": "fc3406c4-967f-41ed-9857-5d3503a0fccf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
              "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
              "       44, 45, 46, 47, 48, 49, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "np.arange(10,51)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZUIzdDeLKww"
      },
      "source": [
        "#### Create an array of all the even integers from 10 to 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVVfz73BLKw5",
        "outputId": "ee44ecb6-9f44-4e9a-d6d5-b78d81207108"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
              "       44, 46, 48, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "np.arange(10,51,2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6e200i5-LKxB"
      },
      "source": [
        "#### Create a 3x3 matrix with values ranging from 0 to 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ODYRVCxLKxF",
        "outputId": "2f606ce3-7772-49d5-f1aa-92c820baf132"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5],\n",
              "       [6, 7, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "np.arange(0,9).reshape(3,3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54wb4NboLKxN"
      },
      "source": [
        "#### Create a 3x3 identity matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8JQg-VSLKxT",
        "outputId": "b5d53b7c-9669-4971-cbf3-c7ac9bacb8bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "np.identity(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_bpkEUARLKxa"
      },
      "source": [
        "#### Use NumPy to generate a random number between 0 and 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Co4I1qACLKxc",
        "outputId": "bce9aa8f-1882-4e91-a2d4-6cce3932ce5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06360810347737544"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "np.random.rand()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Qix8s61LKxp"
      },
      "source": [
        "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUgQBgpQLKxs",
        "outputId": "fe96d75b-faad-4c22-db94-a8d09bd4c1f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.54038712, -0.74530485,  1.18885457, -0.63490707,  1.27617429,\n",
              "        0.0653737 ,  1.08041893,  0.34375148,  2.28438015, -2.09310526,\n",
              "       -0.89043715, -1.0164234 ,  1.26307374,  0.23161553, -0.60054092,\n",
              "        0.26127434,  1.27547807,  0.3573768 , -0.09935628, -1.63338479,\n",
              "        0.85287514,  0.45685337, -1.4563137 ,  0.07119133,  0.81605199])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "np.random.standard_normal(25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-sL9HYlWLKx1"
      },
      "source": [
        "#### Create the following matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2pm5Mm6LKx5",
        "outputId": "504ef372-67be-49fe-c6c3-b1a981a1b19d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 , 0.11,\n",
              "       0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21, 0.22,\n",
              "       0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33,\n",
              "       0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44,\n",
              "       0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54, 0.55,\n",
              "       0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65, 0.66,\n",
              "       0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77,\n",
              "       0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88,\n",
              "       0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "np.arange(1,100) / 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQzj3r7NLKx_"
      },
      "source": [
        "#### Create an array of 20 linearly spaced points between 0 and 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AniTWWz9LKyC",
        "outputId": "c24cf7d5-95d9-4f94-daa9-089ce3526816"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
              "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
              "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
              "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "np.linspace(0,1,20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3vsM3rPLKyK"
      },
      "source": [
        "## Numpy Indexing and Selection\n",
        "\n",
        "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AM_s5FAvLKyN",
        "outputId": "c0cecb8a-05fe-4b8e-aaff-d22d0bd24a77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4,  5],\n",
              "       [ 6,  7,  8,  9, 10],\n",
              "       [11, 12, 13, 14, 15],\n",
              "       [16, 17, 18, 19, 20],\n",
              "       [21, 22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "mat = np.arange(1,26).reshape(5,5)\n",
        "mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "rxlUOQ4dLKyX"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BJ3cylOLKyf",
        "outputId": "90b73cf3-c42f-47fb-e80a-04e5e2382b8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12, 13, 14, 15],\n",
              "       [17, 18, 19, 20],\n",
              "       [22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "mat[2:,1:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "9-U53vNoLKyw"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJyclCVfLKy6",
        "outputId": "4c15d12a-3271-40a7-a7b4-4c1130bff516"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "mat[3,4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "E_PuvjL5LKzH"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzFTd7eHLKzY",
        "outputId": "8ff3cfd0-ccd1-424a-8cfe-5cc7785e635e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2],\n",
              "       [ 7],\n",
              "       [12]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "mat[:3, 1].reshape(3,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "BaybacXxLKze"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvblpMEFLKzl",
        "outputId": "4417f065-8ce5-4190-c726-3369bb6d2e8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([21, 22, 23, 24, 25])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "mat[4,:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "L0SoIZfILKzr"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2NmY81aLKzv",
        "outputId": "a7895327-cd67-4239-8d46-fb32cea55162"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16, 17, 18, 19, 20],\n",
              "       [21, 22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "mat[3:,:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40ijP69QLKz6"
      },
      "source": [
        "### Now do the following"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJYmPKZ6LKz7"
      },
      "source": [
        "#### Get the sum of all the values in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgjWcsNHLKz9",
        "outputId": "da306309-abe3-48c9-a16e-b32f8012c66c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "325"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "mat.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOxAVLe8LK0B"
      },
      "source": [
        "#### Get the standard deviation of the values in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bExi0tvLK0D",
        "outputId": "8b42392a-a129-489f-ae2a-31c8fe43f01e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.211102550927978"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "mat.std()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWvkrLQWLK0H"
      },
      "source": [
        "#### Get the sum of all the columns in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XFw4SD1LK0J",
        "outputId": "038ae16f-a938-43b7-f045-8fa4d29a2491"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 15,  40,  65,  90, 115])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "mat.sum(axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "x75g5hGlLK0N"
      },
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "39110394_JAGADEESH P{Assignment_2} Numpy_Exercise-2.ipynb",
      "provenance": [],
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